Multiclass Brain Tumor Classification Using Convolutional Neural Network and Support Vector Machine

Kibriya, Hareem; Masood, Momina; Nawaz, Marriam; Rafique, Rimsha; Rehman, Safia

doi:10.1109/majicc53071.2021.9526262

Cited by 29 publications

(8 citation statements)

References 16 publications

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“…This paper is a continuation of research presented in Kibriya et al [ 9 ] that compares the performance of two transfer learning architectures such as ResNet18 and GoogLeNet to classify brain tumors from MRI images. The deep features were classified using end-to-end CNN models as well as ECOC based SVM.…”

Section: Introductionmentioning

confidence: 87%

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A Novel and Effective Brain Tumor Classification Model Using Deep Feature Fusion and Famous Machine Learning Classifiers

Kibriya

Amin

Alshehri

et al. 2022

Computational Intelligence and Neuroscience

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Brain tumors are difficult to treat and cause substantial fatalities worldwide. Medical professionals visually analyze the images and mark out the tumor regions to identify brain tumors, which is time-consuming and prone to error. Researchers have proposed automated methods in recent years to detect brain tumors early. These approaches, however, encounter difficulties due to their low accuracy and large false-positive values. An efficient tumor identification and classification approach is required to extract robust features and perform accurate disease classification. This paper proposes a novel multiclass brain tumor classification method based on deep feature fusion. The MR images are preprocessed using min-max normalization, and then extensive data augmentation is applied to MR images to overcome the lack of data problem. The deep CNN features obtained from transfer learned architectures such as AlexNet, GoogLeNet, and ResNet18 are fused to build a single feature vector and then loaded into Support Vector Machine (SVM) and K-nearest neighbor (KNN) to predict the final output. The novel feature vector contains more information than the independent vectors, boosting the proposed method’s classification performance. The proposed framework is trained and evaluated on 15,320 Magnetic Resonance Images (MRIs). The study shows that the fused feature vector performs better than the individual vectors. Moreover, the proposed technique performed better than the existing systems and achieved accuracy of 99.7%; hence, it can be used in clinical setup to classify brain tumors from MRIs.

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Section: Introductionmentioning

confidence: 87%

“…However, the limitation of traditional ML-based algorithms is that they use a hand-crafted feature extraction strategy. The features are extracted from training images before classification [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel and Effective Brain Tumor Classification Model Using Deep Feature Fusion and Famous Machine Learning Classifiers

Kibriya

Amin

Alshehri

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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“…The dataset was gathered from the GitHub website with a total of 2,065 images: 980 normal and 1,085 tumors. Kibriya et al [39] applied two DL models: GoogLeNet and ResNet-18, to diagnose multi-class brain tumors. They also applied an SVM classifier instead the fully connected layer to achieve better accuracy.…”

Section: Designed Cnn Architecturesmentioning

confidence: 99%

A review on detecting brain tumors using deep learning and magnetic resonance images

Al-Ani

Al-Shamma

2023

IJECE

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Early detection and treatment in the medical field offer a critical opportunity to survive people. However, the brain has a significant role in human life as it handles most human body activities. Accurate diagnosis of brain tumors dramatically helps speed up the patient's recovery and the cost of treatment. Magnetic resonance imaging (MRI) is a commonly used technique due to the massive progress of artificial intelligence in medicine, machine learning, and recently, deep learning has shown significant results in detecting brain tumors. This review paper is a comprehensive article suitable as a starting point for researchers to demonstrate essential aspects of using deep learning in diagnosing brain tumors. More specifically, it has been restricted to only detecting brain tumors (binary classification as normal or tumor) using MRI datasets in 2020 and 2021. In addition, the paper presents the frequently used datasets, convolutional neural network architectures (standard and designed), and transfer learning techniques. The crucial limitations of applying the deep learning approach, including a lack of datasets, overfitting, and vanishing gradient problems, are also discussed. Finally, alternative solutions for these limitations are obtained.

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“…The introduced technique accomplished arrangement exactness 91% and Efficiency 92.7%. As per research [18], this paper investigates machine learning & deep learning methods for multiclass brain tumour classification. First, the brain MRI images are classified using ResNet-18 and GoogLeNet end-to-end convolutional neural network models.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Brain Tumor Classification Algorithm using SVM Classifier

Sharma¹,

Nandal²,

Sinhal³

2022

IJETAE

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This study provides an intelligent classification method for distinguishing between abnormal and normal MRI brain images. Medical pictures like MRI, ECG, and CTscan pictures are vital tools for accurately diagnosing human disease. Whenever a lot of MRIs need to be examined, traditional approach of manual tumour analysis, which relies on visual examination by a physician and radiologist, might lead to inaccurate classification. To remove human mistakes, a proposal is made for an intelligent classification system that responds to the essentials of image classification. Brain tumours are one of the primary causes of human mortality. If a tumor is diagnosed appropriately at an early stage, the chances of survival can be improved. The human brain is studied using the MRI method. The acronym MRI stands for magnetic resonance imaging. In this study, classification strategies based on Support Vector Machines (SVM) are proposed and used to brain imaging categorization. In this research, grayscale, symmetry, and texture features are utilised to extract features from MRI images. The fundamental objective of this study is to offer a decent classification result (improved accuracy and lower error rate) to detect MRI brain tumours with help of SVM. Keywords— Brain tumor, Classification, SVM, MRI.

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Multiclass Brain Tumor Classification Using Convolutional Neural Network and Support Vector Machine

Cited by 29 publications

References 16 publications

A Novel and Effective Brain Tumor Classification Model Using Deep Feature Fusion and Famous Machine Learning Classifiers

A Novel and Effective Brain Tumor Classification Model Using Deep Feature Fusion and Famous Machine Learning Classifiers

A review on detecting brain tumors using deep learning and magnetic resonance images

A Novel Brain Tumor Classification Algorithm using SVM Classifier

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